[diffusion] model: support flux Klein (#17173)
This commit is contained in:
Adarsh Shirawalmath
committed by
GitHub
GitHub
parent
daa4841e86
commit
7c39ea68f3
@@ -550,7 +550,11 @@ class Flux2TransformerBlock(nn.Module):
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class Flux2TimestepGuidanceEmbeddings(nn.Module):
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def __init__(
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self, in_channels: int = 256, embedding_dim: int = 6144, bias: bool = False
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self,
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in_channels: int = 256,
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embedding_dim: int = 6144,
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bias: bool = False,
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guidance_embeds: bool = True,
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):
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super().__init__()
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@@ -561,24 +565,32 @@ class Flux2TimestepGuidanceEmbeddings(nn.Module):
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in_channels=in_channels, time_embed_dim=embedding_dim, sample_proj_bias=bias
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)
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self.guidance_embedder = TimestepEmbedding(
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in_channels=in_channels, time_embed_dim=embedding_dim, sample_proj_bias=bias
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)
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if guidance_embeds:
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self.guidance_embedder = TimestepEmbedding(
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in_channels=in_channels,
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time_embed_dim=embedding_dim,
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sample_proj_bias=bias,
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)
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else:
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self.guidance_embedder = None
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def forward(self, timestep: torch.Tensor, guidance: torch.Tensor) -> torch.Tensor:
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def forward(
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self, timestep: torch.Tensor, guidance: Optional[torch.Tensor] = None
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) -> torch.Tensor:
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timesteps_proj = self.time_proj(timestep)
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timesteps_emb = self.timestep_embedder(
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timesteps_proj.to(timestep.dtype)
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) # (N, D)
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guidance_proj = self.time_proj(guidance)
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guidance_emb = self.guidance_embedder(
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guidance_proj.to(guidance.dtype)
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) # (N, D)
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time_guidance_emb = timesteps_emb + guidance_emb
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return time_guidance_emb
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if guidance is not None and self.guidance_embedder is not None:
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guidance_proj = self.time_proj(guidance)
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guidance_emb = self.guidance_embedder(
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guidance_proj.to(guidance.dtype)
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) # (N, D)
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time_guidance_emb = timesteps_emb + guidance_emb
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return time_guidance_emb
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else:
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return timesteps_emb
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class Flux2Modulation(nn.Module):
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@@ -650,8 +662,10 @@ class Flux2Transformer2DModel(CachableDiT, OffloadableDiTMixin):
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axes_dims_rope: Tuple[int, ...] = config.axes_dims_rope
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rope_theta: int = config.rope_theta
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eps: float = config.eps
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guidance_embeds: bool = getattr(config, "guidance_embeds", True)
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self.out_channels = out_channels or in_channels
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self.inner_dim = num_attention_heads * attention_head_dim
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self.guidance_embeds = guidance_embeds
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# 1. Sinusoidal positional embedding for RoPE on image and text tokens
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self.rotary_emb = Flux2PosEmbed(theta=rope_theta, axes_dim=axes_dims_rope)
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@@ -661,6 +675,7 @@ class Flux2Transformer2DModel(CachableDiT, OffloadableDiTMixin):
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in_channels=timestep_guidance_channels,
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embedding_dim=self.inner_dim,
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bias=False,
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guidance_embeds=guidance_embeds,
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)
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# 3. Modulation (double stream and single stream blocks share modulation parameters, resp.)
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@@ -767,7 +782,8 @@ class Flux2Transformer2DModel(CachableDiT, OffloadableDiTMixin):
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# 1. Calculate timestep embedding and modulation parameters
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timestep = timestep.to(hidden_states.dtype)
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guidance = guidance.to(hidden_states.dtype)
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if guidance is not None:
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guidance = guidance.to(hidden_states.dtype)
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temb = self.time_guidance_embed(timestep, guidance)
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422
python/sglang/multimodal_gen/runtime/models/encoders/qwen3.py
Normal file
422
python/sglang/multimodal_gen/runtime/models/encoders/qwen3.py
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@@ -0,0 +1,422 @@
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from collections.abc import Iterable
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from typing import Any
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import torch
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from torch import nn
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from sglang.multimodal_gen.configs.models.encoders import BaseEncoderOutput
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from sglang.multimodal_gen.configs.models.encoders.qwen3 import Qwen3TextConfig
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from sglang.multimodal_gen.runtime.distributed import get_tp_world_size
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from sglang.multimodal_gen.runtime.layers.activation import SiluAndMul
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from sglang.multimodal_gen.runtime.layers.attention import LocalAttention
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from sglang.multimodal_gen.runtime.layers.layernorm import RMSNorm
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from sglang.multimodal_gen.runtime.layers.linear import (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from sglang.multimodal_gen.runtime.layers.quantization import QuantizationConfig
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from sglang.multimodal_gen.runtime.layers.rotary_embedding import get_rope
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from sglang.multimodal_gen.runtime.layers.vocab_parallel_embedding import (
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VocabParallelEmbedding,
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)
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from sglang.multimodal_gen.runtime.loader.weight_utils import (
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default_weight_loader,
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maybe_remap_kv_scale_name,
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)
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from sglang.multimodal_gen.runtime.models.encoders.base import TextEncoder
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class Qwen3MLP(nn.Module):
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"""Qwen3 MLP with SwiGLU activation and tensor parallelism."""
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: QuantizationConfig | None = None,
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bias: bool = False,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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input_size=hidden_size,
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output_sizes=[intermediate_size] * 2,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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input_size=intermediate_size,
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output_size=hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.down_proj",
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)
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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. Only silu is supported."
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)
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self.act_fn = SiluAndMul()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x, _ = self.gate_up_proj(x)
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x = self.act_fn(x)
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x, _ = self.down_proj(x)
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return x
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class Qwen3Attention(nn.Module):
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"""Qwen3 attention with QK-Norm and tensor parallelism.
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Key difference from LLaMA: RMSNorm is applied to Q and K before attention.
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"""
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def __init__(
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self,
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config: Qwen3TextConfig,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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rope_theta: float = 1000000.0,
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rope_scaling: dict[str, Any] | None = None,
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max_position_embeddings: int = 40960,
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quant_config: QuantizationConfig | None = None,
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bias: bool = False,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tp_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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assert self.total_num_kv_heads % tp_size == 0
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else:
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = getattr(
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config, "head_dim", self.hidden_size // self.total_num_heads
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)
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self.rotary_dim = self.head_dim
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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# QKV projection with tensor parallelism
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self.qkv_proj = QKVParallelLinear(
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hidden_size=hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.total_num_heads,
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total_num_kv_heads=self.total_num_kv_heads,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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# Output projection
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self.o_proj = RowParallelLinear(
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input_size=self.total_num_heads * self.head_dim,
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output_size=hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj",
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)
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# QK-Norm: Key difference from LLaMA
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rms_norm_eps = getattr(config, "rms_norm_eps", 1e-6)
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self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
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self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps)
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# Rotary embeddings
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.rotary_dim,
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max_position=max_position_embeddings,
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base=int(rope_theta),
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rope_scaling=rope_scaling,
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is_neox_style=True,
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)
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# Attention with FlashAttention/SageAttn support
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self.attn = LocalAttention(
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self.num_heads,
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self.head_dim,
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self.num_kv_heads,
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softmax_scale=self.scaling,
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causal=True,
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supported_attention_backends=config._supported_attention_backends,
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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# QKV projection
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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# Reshape for QK-norm
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batch_size, seq_len = q.shape[0], q.shape[1]
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q = q.reshape(batch_size, seq_len, self.num_heads, self.head_dim)
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k = k.reshape(batch_size, seq_len, self.num_kv_heads, self.head_dim)
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v = v.reshape(batch_size, seq_len, self.num_kv_heads, self.head_dim)
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# Apply QK-Norm (key difference from LLaMA)
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q = self.q_norm(q)
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k = self.k_norm(k)
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# Reshape back for rotary embeddings
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q = q.reshape(batch_size, seq_len, -1)
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k = k.reshape(batch_size, seq_len, -1)
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# Apply rotary embeddings
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q, k = self.rotary_emb(positions, q, k)
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# Reshape for attention
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q = q.reshape(batch_size, seq_len, self.num_heads, self.head_dim)
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k = k.reshape(batch_size, seq_len, self.num_kv_heads, self.head_dim)
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# Attention
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attn_output = self.attn(q, k, v)
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attn_output = attn_output.reshape(batch_size, seq_len, -1)
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# Output projection
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output, _ = self.o_proj(attn_output)
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return output
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class Qwen3DecoderLayer(nn.Module):
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"""Qwen3 transformer decoder layer."""
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def __init__(
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self,
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config: Qwen3TextConfig,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 1000000.0)
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rope_scaling = getattr(config, "rope_scaling", None)
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max_position_embeddings = getattr(config, "max_position_embeddings", 40960)
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attention_bias = getattr(config, "attention_bias", False)
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self.self_attn = Qwen3Attention(
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config=config,
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=getattr(
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config, "num_key_value_heads", config.num_attention_heads
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),
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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bias=attention_bias,
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prefix=f"{prefix}.self_attn",
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)
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self.mlp = Qwen3MLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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bias=getattr(config, "mlp_bias", False),
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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: torch.Tensor | None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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hidden_states = self.self_attn(positions=positions, hidden_states=hidden_states)
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# MLP
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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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class Qwen3ForCausalLM(TextEncoder):
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"""Qwen3 causal language model for text encoding in diffusion models.
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Features:
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- Tensor parallelism support
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- FlashAttention/SageAttn/SDPA support via LocalAttention
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- QK-Norm for better training stability
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- FSDP sharding for CPU offload
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"""
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def __init__(self, config: Qwen3TextConfig) -> None:
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super().__init__(config)
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self.config = config
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self.quant_config = config.quant_config
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# Embedding layer with tensor parallelism
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if config.lora_config is not None:
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max_loras = getattr(config.lora_config, "max_loras", 1)
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lora_vocab_size = getattr(config.lora_config, "lora_extra_vocab_size", 1)
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lora_vocab = lora_vocab_size * max_loras
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else:
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lora_vocab = 0
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self.vocab_size = config.vocab_size + lora_vocab
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self.org_vocab_size = config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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org_num_embeddings=config.vocab_size,
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quant_config=config.quant_config,
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)
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# Transformer layers
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self.layers = nn.ModuleList(
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[
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Qwen3DecoderLayer(
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config=config,
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quant_config=config.quant_config,
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prefix=f"{config.prefix}.layers.{i}",
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)
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for i in range(config.num_hidden_layers)
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]
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)
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# Final layer norm
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None = None,
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position_ids: torch.Tensor | None = None,
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attention_mask: torch.Tensor | None = None,
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inputs_embeds: torch.Tensor | None = None,
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output_hidden_states: bool | None = None,
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**kwargs,
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) -> BaseEncoderOutput:
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output_hidden_states = (
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output_hidden_states
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if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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if position_ids is None:
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position_ids = torch.arange(
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0, hidden_states.shape[1], device=hidden_states.device
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).unsqueeze(0)
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all_hidden_states: tuple[Any, ...] | None = () if output_hidden_states else None
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for layer in self.layers:
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if all_hidden_states is not None:
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all_hidden_states += (
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(hidden_states,)
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if residual is None
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else (hidden_states + residual,)
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)
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hidden_states, residual = layer(position_ids, hidden_states, residual)
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hidden_states, _ = self.norm(hidden_states, residual)
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# Add hidden states from the last decoder layer
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if all_hidden_states is not None:
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all_hidden_states += (hidden_states,)
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return BaseEncoderOutput(
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last_hidden_state=hidden_states,
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||||
hidden_states=all_hidden_states,
|
||||
)
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
"""Load weights with support for tensor parallelism and weight remapping."""
|
||||
params_dict = dict(self.named_parameters())
|
||||
loaded_params: set[str] = set()
|
||||
|
||||
for name, loaded_weight in weights:
|
||||
# Strip 'model.' prefix from HuggingFace Qwen3 weights
|
||||
if name.startswith("model."):
|
||||
name = name[6:] # len("model.") == 6
|
||||
|
||||
# Skip rotary embedding weights
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
|
||||
continue
|
||||
|
||||
# Handle KV scale remapping
|
||||
if "scale" in name:
|
||||
kv_scale_name: str | None = maybe_remap_kv_scale_name(name, params_dict)
|
||||
if kv_scale_name is None:
|
||||
continue
|
||||
else:
|
||||
name = kv_scale_name
|
||||
|
||||
# Handle stacked params mapping (qkv_proj, gate_up_proj)
|
||||
for (
|
||||
param_name,
|
||||
weight_name,
|
||||
shard_id,
|
||||
) in self.config.arch_config.stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
|
||||
# Skip loading extra bias for GPTQ models
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
|
||||
if name not in params_dict:
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
|
||||
if name not in params_dict:
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
|
||||
loaded_params.add(name)
|
||||
|
||||
return loaded_params
|
||||
|
||||
|
||||
EntryClass = Qwen3ForCausalLM
|
||||
@@ -0,0 +1,8 @@
|
||||
from sglang.multimodal_gen.runtime.pipelines.flux_2 import Flux2Pipeline
|
||||
|
||||
|
||||
class Flux2KleinPipeline(Flux2Pipeline):
|
||||
pipeline_name = "Flux2KleinPipeline"
|
||||
|
||||
|
||||
EntryClass = Flux2KleinPipeline
|
||||
Reference in New Issue
Block a user